Sequential Questioning MCP Server

# Sequential Questioning MCP Server A specialized server that enables LLMs (Large Language Models) to gather specific information through sequential questioning. This project implements the MCP (Model Control Protocol) standard for seamless integration with LLM clients. ## Project Status 🎉 **Version 1.0.0 Released** 🎉 The Sequential Questioning MCP Server is now complete and ready for production deployment. All planned features have been implemented, tested, and documented. ## Features - **Sequential Questioning Engine**: Generates contextually appropriate follow-up questions based on previous responses - **MCP Protocol Support**: Full implementation of the MCP specification for integration with LLMs - **Robust API**: RESTful API with comprehensive validation and error handling - **Vector Database Integration**: Efficient storage and retrieval of question patterns - **Comprehensive Monitoring**: Performance metrics and observability with Prometheus and Grafana - **Production-Ready Deployment**: Kubernetes deployment configuration with multi-environment support - **High Availability**: Horizontal Pod Autoscaler and Pod Disruption Budget for production reliability - **Security**: Network policies to restrict traffic and secure the application ## Documentation - [API Reference](docs/api_reference.md) - [Architecture](docs/architecture.md) - [Usage Examples](docs/usage_examples.md) - [Deployment Guide](docs/deployment.md) - [Operational Runbook](docs/operational_runbook.md) - [Load Testing](docs/load_testing.md) - [Deployment Verification](docs/deployment_verification.md) - [Final Deployment Plan](docs/final_deployment_plan.md) - [Release Notes](docs/project_release_notes.md) ## Getting Started ### Prerequisites - Python 3.10+ - Docker and Docker Compose (for local development) - Kubernetes cluster (for production deployment) - PostgreSQL 15.4+ - Access to a Qdrant instance ### Quick Start The easiest way to get started is to use our initialization script: ```bash ./scripts/initialize_app.sh ``` This script will: 1. Check if Docker is running 2. Start all necessary containers with Docker Compose 3. Run database migrations automatically 4. Provide information on how to access the application The application will be available at http://localhost:8001 ### Local Development 1. Clone the repository ```bash git clone https://github.com/your-organization/sequential-questioning.git cd sequential-questioning ``` 2. Install dependencies ```bash pip install -e ".[dev]" ``` 3. Set up environment variables ```bash cp .env.example .env # Edit .env file with your configuration ``` 4. Run the development server ```bash uvicorn app.main:app --reload ``` ### Docker Deployment ```bash docker-compose up -d ``` ### Database Setup If you're starting the application manually, don't forget to run the database migrations: ```bash export DATABASE_URL="postgresql://postgres:postgres@localhost:5432/postgres" bash scripts/run_migrations.sh ``` ### Kubernetes Deployment 1. Development Environment ```bash kubectl apply -k k8s/overlays/dev ``` 2. Staging Environment ```bash kubectl apply -k k8s/overlays/staging ``` 3. Production Environment ```bash kubectl apply -k k8s/overlays/prod ``` See the [Final Deployment Plan](docs/final_deployment_plan.md) and [Operational Runbook](docs/operational_runbook.md) for detailed instructions. ## Monitoring Access Prometheus and Grafana dashboards for monitoring: ```bash kubectl port-forward -n monitoring svc/prometheus 9090:9090 kubectl port-forward -n monitoring svc/grafana 3000:3000 ``` ## CI/CD Pipeline Automated CI/CD pipeline with GitHub Actions: - Continuous Integration: Linting, type checking, and testing - Continuous Deployment: Automated deployments to dev, staging, and production - Deployment Verification: Automated checks post-deployment ## Testing Run the test suite: ```bash pytest ``` Run performance tests: ```bash python -m tests.performance.test_sequential_questioning_load ``` ## Troubleshooting ### Database Tables Not Created If the application is running but the database tables don't exist: 1. Make sure the database container is running 2. Run the database migrations manually: ```bash export DATABASE_URL="postgresql://postgres:postgres@localhost:5432/postgres" bash scripts/run_migrations.sh ``` ### Pydantic Version Compatibility If you encounter the error `pydantic.errors.PydanticImportError: BaseSettings has been moved to the pydantic-settings package`, ensure that: 1. The `pydantic-settings` package is included in your dependencies 2. You're importing `BaseSettings` from `pydantic_settings` instead of directly from `pydantic` This project uses Pydantic v2.x which moved `BaseSettings` to a separate package. ## Contributing Contributions are welcome! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines. ## License [MIT License](LICENSE) ## Contact For support or inquiries, contact support@example.com